Artificial neural networks are powerful information processors conceptually based on the field of neurobiology as is well known in the art. Such networks strive to simulate biological neural network functions in computer architectures. In other words, an artificial neural network must correlate a wide variety of inputs to produce an output. One such biological neural network function is that of image or pattern recognition. While image recognition covers a variety of applications, several will be described briefly in order to highlight problems associated therewith. The applications include satellite image registration, digital scene correlation for autonomous vehicle positional updates and the air reconnaissance six degree of freedom object identification problem.
Satellite images are used extensively in both civilian and military applications. Individual satellite images must be correlated or registered with one another in order to provide a coherent image of an area of interest. For example, one image of an area may be obstructed in one portion thereof while a second later image may be obstructed in another portion thereof as the satellite orbits the earth. It may be necessary to use several images of an area of interest to piece together one coherent image free from any obstructions. Unfortunately, as each individual image is overlaid on top of one another, boundaries of objects and obstructions within the area of interest do not line up from one image to the next. Accordingly, the image registration problem must be solved in order to align the boundaries of the objects to generate a coherent satellite image.
Autonomous vehicle (e.g. robots, missiles, etc.) positional updates typically rely on comparing the actual image of an area being traversed with a reference image of the area. Unfortunately, the autonomous vehicle is frequently on a course whereby the actual image is formed at an angle with respect to the angle used to generate the reference image. It is thus necessary to rotate the actual image to match the reference image in order for the vehicle to update its position. This problem is known as digital scene correlation. Accordingly, it becomes necessary to correlate the actual image so that it is aligned with the reference image.
Air reconnaissance for purposes of ground object identification typically compares an actual image of a ground object with a database of reference models in order to classify the ground object. Unfortunately, the angle and distance of the reconnaissance aircraft causes the actual image of the ground object to be distorted in size and/or shape with respect to the reference models. This distortion may occur in any one or more of six degrees of freedom. In order to classify the ground object, it becomes necessary to maximally align the actual image (which has undergone an unknown affine transformation) with one of the reference models. However the aforementioned size and/or shape distortions result in boundary misalignments between the actual image and reference models.